8 top New Relic alternatives to break the pricing trap (2026 guide)

"Observability is a team sport, until the bill arrives." - Yours truly.

For many engineering organizations, New Relic is the default starting point. Its generous free tier is well known, but as startups mature into enterprises, the platform’s pricing structure often becomes a barrier. Engineering leaders are frequently forced into an uncomfortable position: deciding which engineers are "senior enough" to deserve a login, or risking unpredictable bills from "consumption" models that charge for every query.

The hidden cost isn't on the invoice. It's the cost of exclusion. When junior developers, support agents, and product managers are locked out of data to save money, incident response slows, and knowledge becomes centralized among a small number of licensed users.

This guide explores 8 alternatives that break you free.

What are the hidden costs of New Relic's "walled garden"? #

New Relic is a robust, feature-rich platform that pioneered Application Performance Monitoring (APM). However, its architecture and business model were built for a previous era of software development where "monitoring" was the job of a specialized Ops team, not a shared responsibility across the entire organization.

The pricing trap #

New Relic forces you to choose between different pricing models, neither of which aligns with customers' observability needs.

The "seat tax": punishing collaboration with per-user pricing #

The defining struggle with New Relic is the distinction between "Basic" and "Full Platform" users. Modern observability relies on the idea that anyone should be able to query logs or trace a request to understand a bug. New Relic’s model penalizes this behavior. This model monetizes users, not data, incentivizing leaders to ration access.

The “compute tax”: forcing users to restrict queries and actions #

The alternative "Core Compute" model removes seat limits but introduces opacity in how consumption is billed. They charge based on "Compute Capacity Units" (CCUs) - a proprietary metric used for actions such as refreshing a dashboard, loading a page, or running a query. As a result, users are forced to reduce their use of observability tools to avoid a huge bill. This model also adds friction to investigations because developers and SREs need to think about how they use the product while troubleshooting, especially during high-pressure incidents when the focus should be on resolving the issue rather than on tool costs. SREs should not be constrained as to how a product is used when resolving incidents.

The "intelligence" tax: penalizing AI workflows #

As teams move toward AI-driven observability, New Relic has introduced a new cost vector: Advanced Compute Units (aCCUs). Features such as predictive alerting and AI-assisted root cause analysis consume these premium units more frequently. This means the more you use modern AI tools to debug your system, the more you pay. The potential for AI agents to issue 10s, if not 100s of queries, makes this pricing model completely inappropriate, with the potential for unpredictable costs.

Vendor lock-in: the trap of a proprietary query language (NRQL) #

Proprietary languages like NRQL are dangerous precisely because they look like SQL, but they don't behave like it. While NRQL borrows keywords such as SELECT and FROM, it lacks the full power of standard SQL (e.g., complex joins and standard functions) and, most importantly, is non-portable.

Spending years mastering NRQL creates a "dead-end skill." You cannot migrate those queries to another tool, a data warehouse, or a business intelligence platform.

The "black box" problem: when you don't own your data #

New Relic operates as a black box. You send data through their agents and query it in their language. If you ever decide to leave, you realize that your dashboards, alerts, and team knowledge are non-portable. Open alternatives are shifting toward OpenTelemetry for data collection and using existing SQL skills for querying. This "Open Architecture" ensures that you own your data pipeline and your team’s skills remain relevant regardless of the vendor.

The modern solution: an open, SQL-based observability platform #

Introducing the ClickStack model: built on ClickHouse #

The ClickStack model is a different philosophy from New Relic's. It's a model in which a high-performance data engine, ClickHouse, serves as the core, and an open-source visualization layer, such as HyperDX, provides the user interface.

Moving to an open engine doesn't mean sacrificing user experience. HyperDX provides an interface that rivals SaaS platforms like New Relic or Datadog.

• Natural Language Querying: Ask plain English questions about your logs and traces without SQL expertise. The system translates them into optimized queries.
• Correlated Traces and Logs: Like New Relic, HyperDX lets you jump from a trace spanning multiple microservices directly to the relevant logs.
• Full SQL Power: When you need more than the standard UI allows, you can use standard SQL to run complex aggregations that proprietary tools can't match.

Instead of buying "seats" in a SaaS application, you're using a powerful, open database. This architecture is designed to democratize observability. Because ClickHouse is a columnar database built for analytics, it is architecturally well-suited to the aggregation-heavy, high-cardinality queries common in observability, delivering excellent speed and cost efficiency.

Functionally, the ClickStack model replaces the proprietary NRQL with standard SQL. ClickHouse achieves its speed not just from its columnar storage format, but also from vectorized query execution, parallel processing across all available cores, and aggressive data skipping enabled by its sparse primary indexes and data ordering. As OpenAI discovered, the performance is real: “Previously, querying the last 10 minutes would take 1-2 minutes. With ClickStack, it was just a case of how fast I could blink."

Because the pricing is based on the resources required to process your data, users are free. You can invite your entire engineering, product, and support teams to the platform without increasing your monthly spend by a cent. This approach supports modern DevOps goals, like shared responsibility. This model changes your observability strategy from 'what can we afford to sample?' to 'send everything and ask any question'.

This isn't just theory. Industry leaders are already using ClickHouse for observability at extreme scale. Anthropic relies on ClickHouse to scale observability for developing models like Claude 3, noting that with ClickHouse, 'money is not on fire.'

How this model solves New Relic's core problems #

While a ClickHouse-based architecture presents a fundamental shift, other popular tools offer different trade-offs in solving New Relic's challenges.

How do other popular alternatives compare? #

We have selected these alternatives based on their ability to replace New Relic’s specific strengths (APM) while comparing them on key factors like user pricing, data ownership, and query language.

1. Datadog: for an infrastructure-first approach #

If New Relic is the king of APM (Code), Datadog is the king of Infrastructure (Servers). For many teams, the "New Relic vs. Datadog" debate comes down to where your problems live.

• The shift: if your team is spending more time debugging Kubernetes clusters, microservices networking, and cloud infrastructure than distinct lines of code, Datadog’s infrastructure-first approach feels more intuitive than New Relic’s application-first view.
• Key considerations: Datadog does not solve the cost issue. It just moves it. While you might save on "Seat" licenses (Datadog is generally more generous with user counts), you will likely pay more for custom metrics and host-based licensing.
• Best for: teams migrating from a monolithic app (New Relic’s sweet spot) to a complex microservices architecture.

2. Dynatrace: for AI-powered, automated root-cause analysis #

Dynatrace is often the choice for organizations that find New Relic too "manual." While New Relic provides tools for developers to query data, Dynatrace uses its "Davis" AI engine to identify issues automatically.

• The shift: this is for enterprises that want to move away from "dashboards" entirely. The "OneAgent" approach is less about developer curiosity and more about automated root-cause analysis.
• Key considerations: It is arguably more expensive and more proprietary than New Relic. You are doubling down on the "Black Box" model, not escaping it.
• Best for: large-scale, legacy enterprise environments where manual debugging is impossible due to complexity.

3. Grafana Stack: for a DIY, open-source approach #

For teams who resent the "SaaS Tax" entirely, the Grafana ecosystem is the standard exit ramp.

• The shift: this is the "Build vs. Buy" alternative. New Relic sells you a service, while Grafana gives you the components to build it yourself. It offers the ultimate freedom in visualization. New Relic’s dashboards feel rigid and limited compared to what a skilled engineer can build in Grafana.
• Key considerations: you trade subscription costs for operational costs. You are now responsible for the uptime of your monitoring stack.
• Best for: engineering teams with strong OPS resources who value control and visualization above all else.

4. Splunk: for consolidating with a security-first platform #

Sometimes, the pressure to leave New Relic comes from the C-Suite asking to consolidate tools. Splunk is often already in place for Security Information and Event Management (SIEM).

• The shift: Splunk is a log-centric platform. If your debugging workflow is 90% "searching massive log files" and only 10% "looking at APM traces," Splunk’s query power (SPL) is unmatched.
• Key considerations: Splunk is generally not considered "developer-friendly" for APM. It is a heavy, complex tool that lacks the fluidity of New Relic’s trace visualization.
• Best for: consolidating Observability and Security budgets into one massive contract.

5. Chronosphere: for taming high-cardinality metrics at scale #

Chronosphere is a specialized tool that attacks the scaling limitations of Prometheus.

• The shift: New Relic can struggle with "high cardinality" metrics (custom tags). Chronosphere is designed to address this problem by enabling you to shape and aggregate data before it reaches the database.
• Key considerations: It is highly focused on metrics and tracing. If you rely heavily on New Relic’s "Logs in Context," you may find Chronosphere’s logging capabilities less integrated.
• Best for: cloud-native "Unicorns" who are scaling out of a DIY Prometheus setup.

6. Signoz: for an open-source, all-in-one experience #

Signoz is the closest thing to an "Open Source New Relic." It mimics the UI and workflows of the major SaaS players but is built on Open Source ClickHouse.

• The shift: it offers a familiar feel for developers leaving New Relic (traces, metrics, and logs in one pane), but allows you to self-host or pay a much lower cloud rate compared to New Relic. It is an "application first" platform, meaning you interact with the SigNoz UI, not the database directly.
• Key considerations: while the core features are there, it lacks the depth of New Relic’s 15 years of edge-case handling. Additionally, while its self-hosted option provides maximum control, it places the operational burden on your team. The managed Signoz Cloud offering, while convenient, may not match the architectural advantages of other managed ClickHouse services, such as the complete separation of storage and compute that optimizes costs for long-term data retention.
• Best for: startups that want a cheaper, open-source version of New Relic but are comfortable with an ingest-based pricing model, rather than owning the data engine themselves.

7. Dash0: for a backend-only, OTel-native data lake #

Dash0 is for the "Serverless" generation. It is an observability data lake that is strictly OpenTelemetry native.

• The shift: New Relic requires setup, agent configuration, and index management. Dash0 aims to be invisible: you just point your OTel exporter at it, and it handles the rest. For visualization, it relies on the CNCF project Perses, ensuring your dashboards are standards-compliant and not tied to a proprietary vendor.
• Key considerations: it is a newer player with a "bring your own dashboard" philosophy.
• Best for: teams that want a "backend only" replacement for New Relic.

Conclusion: are you renting your data or owning it? #

When you choose an alternative to New Relic, you aren't just selecting a UI. You are choosing a philosophy.

Moving to a modern data warehouse model (ClickStack, Signoz) means asserting ownership. By standardizing on OpenTelemetry for ingestion and the universal SQL standard for analysis, you break down data silos. You allow every developer to access the system at no license fee, and you ensure your team's skills are transferable.

The future of observability isn't about better agents; it's about open data. And when that open data lives in a powerful analytical engine like ClickHouse, you can correlate system performance directly with business outcomes. This goal is impractical when observability and analytics are treated as separate silos.